The Future of Artificial Intelligence Is Coaching – Not Crutches

Who is this article for?

• Technology companies developing AI solutions
• Organizations in industries like healthcare, finance, manufacturing, retail, and transportation looking to adopt AI
• Government agencies and policymakers regulating AI development and use
• Chief Information Officers, Chief Technology Officers, IT Directors, and Innovation Managers responsible for AI strategy and implementation
• Data Scientists, Machine Learning Engineers, and AI Researchers working on developing AI systems
• These roles are critical in shaping the responsible development and deployment of AI to ensure it benefits society while mitigating potential negative impacts.

The Rapid Evolution of AI is Transforming Industries

Artificial intelligence, the ability of computer systems to interpret data, learn, and achieve goals through flexible adaptation, is one of the most transformative technologies of our time (Kaplan & Haenlein, 2019). AI is already the main driver behind emerging technologies like big data, robotics, and the Internet of Things, and is now expanding possibilities with generative AI.

AI adoption is accelerating, with 42% of organizations integrating AI into operations and 40% considering it (IBM, 2023). Generative AI is being implemented by 38% of organizations with another 42% considering it. AI is poised to reshape industries from healthcare and finance to manufacturing and transportation.

Since the 1950s, AI has made remarkable progress – from the first AI checkers program to IBM’s Deep Blue defeating a chess grandmaster in 1997 and Watson winning Jeopardy! in 2011. The latest chapter in AI’s evolution is generative AI, with models like GPT-4 that can engage in human-like communication and content generation.

AI is already being used for breakthrough applications like RNA sequencing for vaccines (Baidu Research, 2022) and modeling human speech (MIT, 2022). The focus is increasingly shifting to perception, reasoning and generalization.

How Will AI Impact the Future?

The impact of AI will be far-reaching and profound. Here are some of the key ways AI is expected to shape the future:

Transforming Business and Work

AI will enable greater automation and optimization of business processes and decision-making. Over half of organizations have adopted AI to some degree, using it for applications like chatbots for customer service, data analysis for strategic insights, and intelligent workflows to streamline operations (Mendelson, 2023).

However, this is raising concerns about potential job losses due to AI and automation. While some jobs, especially routine and manual ones, are at risk, AI is expected to be a net job creator (World Economic Forum, 2023). AI will also augment many jobs, requiring workers to upskill and work alongside AI systems (Nahrstedt, 2023). Successfully managing this transition will require significant investments in education and retraining.

Enabling Scientific Breakthroughs

AI is a powerful tool for scientific research, helping analyze massive datasets, model complex systems, and generate novel insights. It is already enabling breakthroughs in fields like climate science, drug discovery, materials science, and genomics (Goralski & Tan, 2020).

For example, AI is being used to identify new drug candidates, predict the 3D structures of proteins, track deforestation from satellite imagery, and model the impacts of climate change. As AI capabilities grow, it could help solve some of humanity’s greatest challenges.

Shaping New Products and Services

AI is being embedded into a new generation of products and services, enabling personalized recommendations, predictive maintenance, autonomous systems, and much more. Examples range from AI-powered wearables that monitor health to intelligent household products to self-driving vehicles (Puntoni et al., 2020).

For consumers, AI promises greater convenience, customization and automation. However, it also raises concerns about data privacy, algorithmic bias and transparency that need to be addressed (Puntoni et al., 2020).

Impacting Sustainability and Climate Change

AI has significant potential to advance sustainability and mitigate climate change. It can optimize energy systems, improve efficiency, reduce waste, and help model climate impacts. AI is being applied to smart grids, precision agriculture, sustainable manufacturing, and more (Vaio et al., 2020).

However, the growing use of AI itself can have a substantial carbon footprint, with data centers powering AI models set to increase emissions by 80% (The Guardian, 2024). Ensuring AI is developed sustainably will be critical to achieving a net positive impact.

Risks and Challenges of AI

While the potential of AI is immense, it also comes with significant risks that need to be carefully managed:

• Algorithmic bias and fairness – AI systems can reflect the biases in their training data and development, leading to unfair outcomes, especially for already marginalized groups (Kaplan & Haenlein, 2019). Ensuring AI is developed and used responsibly is critical.
• Data privacy and security – The data used to train and run AI systems can expose personal information if not handled properly. 48% of businesses have input non-public information into generative AI tools and 69% are worried about intellectual property risks (Cisco, 2024).
• Transparency and accountability – As AI systems become more complex, it can be difficult to understand how they make decisions and to hold them accountable (Duan et al., 2019). Developing explainable and auditable AI is an important challenge.
• Malicious use – AI can be used for harmful ends, like generating fake content, conducting cyberattacks, or automating weapons (Brougham & Haar, 2017). Proper governance and security measures are needed to mitigate these risks.
• Existential risk – Some worry about advanced AI becoming uncontrollable and posing an existential threat to humanity, although current systems are still narrow in scope (Gyongyosi, 2023). Ensuring AI remains beneficial as it becomes more capable is a key priority.

How Tallyfy Enables Responsible AI Deployment

Tallyfy is a workflow automation platform that helps organizations leverage AI capabilities in a responsible and effective way:

• Explain it once – AI-driven documentation – Easily document AI models, datasets, and processes to ensure transparency and knowledge sharing across the organization. Tallyfy’s AI helps generate clear documentation to keep everyone informed.
• Real time tracking – Track the performance of AI models and workflows to quickly identify issues and make adjustments. Tallyfy provides a real-time view to keep AI efforts on track.
• If this then that – set amazingly simple and powerful conditional rules – Implement governance rules and checkpoints into AI workflows to ensure proper reviews and risk mitigation steps are followed.
• Structure intake – Standardize how data is collected and fed into AI pipelines to improve data quality and consistency. Tallyfy’s forms and checklists make it easy to collect structured data.

By providing a centralized platform to plan, execute, and monitor AI initiatives, Tallyfy helps organizations ensure responsible practices and drive successful outcomes with AI.

• Pursuing AI without a clear strategy and use case based on solving actual business problems and delivering real value
• Failure to assess and mitigate potential risks and unintended consequences of AI systems, such as bias, privacy violations, security risks, etc.
• Lack of transparency and explainability around how AI models work, make decisions, and will be monitored and improved over time
• Insufficient governance structures and practices to ensure responsible development, deployment and use of AI in line with organizational values and ethics
• Underestimating the change management, upskilling, and process redesign needed to effectively integrate AI and get buy-in and adoption from stakeholders

The future of artificial intelligence is not about replacing human intelligence, but rather augmenting and enhancing it. AI can automate routine tasks and provide powerful insights, but it still requires human judgment and oversight to ensure it is developed and used in a responsible and beneficial way.

Organizations need to take a strategic approach to AI, with strong governance and change management to navigate the risks and realize the benefits. Tallyfy can help by providing a platform to document, track, and execute AI initiatives in a structured and transparent way.

By proactively shaping the development of AI, we can create a future where it improves our lives and helps solve major challenges, while mitigating the potential downsides. It will take ongoing collaboration between technologists, domain experts, policymakers, ethicists and society at large, but the potential is immense if we get it right.

How Will AI Transform Our World in the Coming Decades?

Artificial intelligence (AI) is rapidly expanding and becoming an increasingly attractive topic for research and innovation. AI can be defined as a system’s ability to correctly interpret external data, learn from that data, and use those learnings to achieve specific goals through flexible adaptation (Kaplan & Haenlein, 2019). The rise of super computing power and big data technologies in recent years have empowered AI to make significant strides.

The impact of AI could be truly transformative, with industries ranging from finance, healthcare, manufacturing, retail, supply chain, logistics and utilities all potentially being disrupted by the onset of AI technologies (Dwivedi et al., 2021). AI offers the potential to augment and even replace many human tasks and activities within a wide range of applications. The pace of change is staggering, with breakthroughs in machine learning and autonomous decision-making creating opportunities for continued innovation.

What Are the Key Areas Where AI Could Have the Biggest Impact?

Some of the key areas where AI is expected to have a profound impact include:

• Decision-making: AI-based systems are increasingly being used to support or even replace human decision makers, by rapidly analyzing vast amounts of data (Duan et al., 2019). This could transform how decisions are made in businesses, governments and other organizations.
• Customer experience: AI enables highly personalized and efficient customer interactions through technologies like smart chatbots and recommendation engines (Ameen et al., 2021). This could radically change how brands engage with their customers.
• Supply chain optimization: AI can enhance supply chain planning, execution and risk management by processing huge datasets to generate predictive insights (Baryannis et al., 2018). This could create more responsive, resilient and optimized supply chains.
• Sustainable development: AI has the potential to accelerate progress on the UN Sustainable Development Goals by supporting areas like smart cities, precision agriculture, clean energy and beyond (Goralski & Tan, 2020). However, the sustainability impacts of AI itself must also be carefully managed.

What Are the Potential Risks and Challenges of AI Advancement?

While the potential of AI is immense, its rapid rise also creates risks and challenges that need to be navigated:

• AI may automate many jobs and exacerbate technological unemployment if the workforce doesn’t have the skills to adapt (Brougham & Haar, 2017). Proactive policies and corporate strategies will be needed to reskill workers.
• Delegating decisions to AI systems also creates risks around transparency, bias, privacy and security. Robust governance frameworks are needed to ensure AI remains ethical and trustworthy (Puntoni et al., 2020).
• The “black box” nature of some AI systems can make it difficult to understand how they arrived at decisions. Improving the explainability of AI is an important research priority (Dwivedi et al., 2021).

How Could AI Change Business Models and Value Creation in the Future?

AI is likely to drive the emergence of new business models and paradigms for how organizations create and capture value (Vaio et al., 2020). For example:

• AI may enable much more personalized products and services that are dynamically optimized for each user based on their data. This could support premium pricing for customized offerings.
• AI could be used to develop new revenue streams from the data and insights generated by a company’s operations and customers. Monetizing AI capabilities may become a key source of competitive advantage.
• The automation of routine cognitive work by AI may push human workers up the value chain into higher-level roles requiring creativity, empathy, leadership and other uniquely human skills. This will change the talent needs and value proposition of employers.

In summary, AI heralds a new technological age that will profoundly reshape business and society in the coming decades. To thrive in an AI-driven future, organizations will need robust strategies to harness the opportunities while mitigating the potential risks. By proactively shaping the development of AI, we can work towards a future where the benefits are broadly distributed and the technology genuinely improves the human condition.

Tallyfy Tango – A cheerful and alternative take

Scene: Two friends, Ava and Ethan, are having coffee and musing about the future.

Ava: So, what do you think the world will be like in 20 years with all this AI stuff happening?

Ethan: Oh man, it’s going to be wild! I bet we’ll have robot butlers bringing us coffee and giving us daily news briefings.

Ava: (laughs) Right, and self-driving cars will chauffeur us everywhere while we sit back and watch Netflix. We’ll never have to lift a finger!

Ethan: Actually, I read that AI might create more jobs than it eliminates. All these smart systems will still need human oversight and decision-making. We’ll work alongside AI, not be replaced by it.

Ava: That’s a relief. I was worried my job would be taken over by some algorithm. But humans are still needed for the really important stuff, like deciding what show to binge watch next.

Ethan: (chuckles) Definitely. But in all seriousness, I think AI will help solve a lot of big challenges, like curing diseases, reversing climate change, and expanding our knowledge as a species. The future is bright!

Ava: I hope you’re right. As long as the robots don’t rise up and enslave us all, I’m excited to see what unfolds. The future of artificial intelligence should be pretty amazing.

Ethan: Agreed. Now let’s have our robot butler top off these lattes. Hey Siri…

Related Questions

What is artificial intelligence in the future?

In the future, artificial intelligence is expected to become more advanced, autonomous, and integrated into various aspects of our lives. AI systems will likely be capable of handling complex tasks, making decisions, and even exhibiting human-like qualities such as creativity and empathy. As AI continues to evolve, it has the potential to revolutionize industries, transform the way we work and live, and help solve some of the world’s most pressing challenges.

What will AI be like in 2050?

By 2050, AI could reach a level of sophistication that surpasses human intelligence in many domains. It may be capable of self-improvement, leading to exponential growth in its capabilities. AI-powered robots and virtual assistants might become commonplace, assisting us in our daily lives. Additionally, AI could play a crucial role in fields such as healthcare, education, and scientific research, enabling groundbreaking discoveries and personalized solutions tailored to individual needs.

What is the next future of AI?

The next stage in the future of AI is likely to involve the development of artificial general intelligence (AGI) – AI systems that can perform any intellectual task that a human can. This would mark a significant milestone, as current AI is mainly narrow or specialized in specific domains. Beyond AGI, the ultimate goal is to create artificial superintelligence (ASI), which would surpass human intelligence in virtually all areas. However, the path to AGI and ASI is complex and raises important ethical and societal questions that need to be addressed.

What is the future of AI in 2030?

By 2030, AI is expected to be deeply integrated into our daily lives and various industries. It will likely automate many tasks, leading to increased efficiency and productivity. AI-powered personal assistants may become more sophisticated, anticipating our needs and preferences. In healthcare, AI could enable early detection of diseases and personalized treatment plans. Additionally, AI may play a significant role in tackling climate change, optimizing energy consumption, and developing sustainable solutions.

How will artificial intelligence change the future of work?

Artificial intelligence will profoundly impact the future of work. As AI systems become more advanced, they will automate many routine and repetitive tasks, leading to job displacement in certain sectors. However, AI will also create new job opportunities, particularly in fields related to AI development, maintenance, and oversight. The nature of work will shift, with a greater emphasis on skills that complement AI, such as creativity, critical thinking, and emotional intelligence. Adapting to this new landscape will require reskilling and lifelong learning to ensure that workers can thrive alongside AI in the future workplace.

References and Editorial Perspectives

Kaplan A, Haenlein M. Siri, Siri, in My Hand: Who’s the Fairest in the Land? On the Interpretations, Illustrations, and Implications of Artificial Intelligence. Business Horizons. 2019;62:15-25. https://doi.org/10.1016/j.bushor.2018.08.004

Summary of this study

This paper provides a nuanced look at artificial intelligence (AI), classifying it into different evolutionary stages and types of systems. Through case studies, it examines the potential and risks of AI in universities, corporations and governments. The paper presents a framework called the Three C Model to help organizations consider the internal and external implications of AI in terms of confidence, change and control.

Editor perspectives

As a workflow automation platform, we find this study fascinating in how it breaks down AI into more granular stages and types. The Three C Model provides an insightful lens for organizations to evaluate AI’s impact on their operations, people and processes. Understanding the nuances of AI is key as we help companies leverage it to streamline and scale their workflows.

Duan Y, Edwards JS, Dwivedi YK. Artificial Intelligence for Decision Making in the Era of Big Data – Evolution, Challenges and Research Agenda. International Journal of Information Management. 2019;48:63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021

Summary of this study

This paper examines the evolution of AI and its use in decision making in the era of Big Data. It discusses the challenges of integrating AI with human decision makers and proposes a research agenda for information systems scholars. Key areas include conceptual development, human-AI interaction, and implementation of AI systems to support or replace human decision making.

Editor perspectives

The interplay between AI and human decision making is a critical issue as we help organizations automate their workflows. This study provides valuable perspective on the challenges and research opportunities in this space. Finding the right balance and interfaces for AI and human collaboration in operational decision making will be an ongoing question as the technology advances.

Dwivedi YK, Hughes L, Ismagilova E, et al. Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy. International Journal of Information Management. 2021;57:101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

Summary of this study

This study brings together insights from experts across business, government, public sector, and science and technology to examine the opportunities, impacts, challenges and research agenda posed by the rapid emergence of AI. It looks at how AI could disrupt industries from finance and healthcare to manufacturing and logistics. The paper considers AI’s potential to accelerate progress on sustainable development goals as well as risks of economic and social upheaval.

Editor perspectives

The broad, multidisciplinary view this study takes on AI’s impact is illuminating for us as we work with a wide range of industries. The potential for AI to disrupt and transform workflows exists in nearly every sector. At the same time, the societal and economic implications can’t be ignored. As we innovate with AI, maintaining a holistic perspective is crucial.

Puntoni S, Reczek RW, Giesler M, Botti S. Consumers and Artificial Intelligence: An Experiential Perspective. Journal of Marketing. 2020;85:131-151. https://doi.org/10.1177/0022242920953847

Summary of this study

This research examines the consumer experience with AI through four lenses: data capture, classification, delegation, and social interaction. While acknowledging the potential benefits to consumers from AI-enabled products and services, the authors also explore the individual and social challenges that can emerge. They discuss ways organizations can address gaps between their AI investments and consumer value, and map out an agenda for future research.

Editor perspectives

Understanding the end-user experience with AI is hugely important to us as we build out workflow automation capabilities for our customers. This study provides an insightful framework for considering the different touchpoints and tensions in consumer interactions with AI systems. Proactively addressing consumer concerns and creating true value will be an ongoing challenge as AI pervades more and more services.

Baryannis G, Validi S, Dani S, Antoniou G. Supply Chain Risk Management and Artificial Intelligence: State of the Art and Future Research Directions. International Journal of Production Research. 2018;57:2179-2202. https://doi.org/10.1080/00207543.2018.1530476

Summary of this study

This comprehensive literature review looks at applications of AI techniques to supply chain risk management (SCRM). It examines how AI approaches from mathematical programming to machine learning and big data analytics are being used to identify, assess and mitigate supply chain risks. The authors map out the current research landscape and propose an agenda for future work at the intersection of SCRM and AI.

Editor perspectives

Supply chain risk is top of mind for many of the companies we work with, especially in the wake of COVID-19 disruptions. This study highlights how AI and workflow automation can help organizations proactively manage supply chain vulnerabilities and build resilience. We see huge potential for predictive AI to help our customers anticipate and adapt to supply chain risks.

Glossary of terms

Artificial intelligence (AI)

The ability of computer systems to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI systems can interpret data, learn from it, and adapt to achieve specific goals.

Machine learning

A subset of AI that focuses on developing algorithms and statistical models that enable computer systems to improve their performance on a task without being explicitly programmed. Machine learning systems learn from patterns in data to make decisions or predictions.

Deep learning

A more advanced type of machine learning that uses artificial neural networks with many layers to process and learn from vast amounts of data. Deep learning has powered breakthroughs in areas like computer vision, natural language processing, and autonomous systems.

Big data

Extremely large, complex datasets that can be analyzed computationally to reveal patterns, trends and associations. The rise of big data has fueled the development of more sophisticated AI and machine learning techniques to extract insights and inform decision making.

Autonomous systems

AI-powered systems that can perform tasks or make decisions on their own, without human intervention. Examples include self-driving vehicles, intelligent robots, and automated trading systems. Autonomous systems rely on continuous, real-time data processing and adaptation.